Benchmarking reports

from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	9.347988
daal4py_KNeighborsClassifier	0.0	2.0	29.214814
KNeighborsClassifier_kd_tree	0.0	2.0	42.677676
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	28.640284
KMeans_tall	0.0	0.0	23.243623
daal4py_KMeans_tall	0.0	0.0	8.875039
KMeans_short	0.0	0.0	2.758651
daal4py_KMeans_short	0.0	0.0	1.400986
LogisticRegression	0.0	0.0	20.219920
daal4py_LogisticRegression	0.0	0.0	4.352850
Ridge	0.0	0.0	11.123086
daal4py_Ridge	0.0	0.0	2.058851
HistGradientBoostingClassifier	0.0	6.0	17.118189
lightgbm	0.0	5.0	22.952890
xgboost	0.0	5.0	6.414030
catboost	0.0	5.0	0.956584
total	1.0	3.0	51.442933

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.231	0.000	3.465	0.000	1	5	NaN	NaN	0.508	0.000	0.455	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.161	0.228	0.000	0.024	1	5	0.831	0.704	1.754	0.039	13.776	0.335	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.225	0.002	0.000	0.225	1	5	1.000	1.000	0.095	0.001	2.384	0.026	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.000	5.536	0.000	-1	5	NaN	NaN	0.496	0.000	0.291	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	36.224	0.000	0.000	0.036	-1	5	0.831	0.942	1.805	0.013	20.073	0.147	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.200	0.012	0.000	0.200	-1	5	1.000	1.000	0.095	0.001	2.107	0.125	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.146	0.000	5.496	0.000	-1	1	NaN	NaN	0.499	0.000	0.292	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	26.064	0.171	0.000	0.026	-1	1	0.714	0.819	1.772	0.021	14.707	0.200	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.013	0.000	0.183	-1	1	1.000	1.000	0.094	0.001	1.935	0.144	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.150	0.000	5.345	0.000	1	100	NaN	NaN	0.497	0.000	0.301	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.273	0.067	0.000	0.024	1	100	0.939	0.704	1.764	0.044	13.762	0.344	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.227	0.002	0.000	0.227	1	100	1.000	1.000	0.094	0.000	2.406	0.027	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.140	0.000	5.728	0.000	-1	100	NaN	NaN	0.496	0.000	0.282	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	36.390	0.000	0.000	0.036	-1	100	0.939	0.942	1.809	0.012	20.113	0.129	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.198	0.014	0.000	0.198	-1	100	1.000	1.000	0.095	0.001	2.078	0.144	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.140	0.000	5.707	0.000	1	1	NaN	NaN	0.498	0.000	0.282	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.829	0.028	0.000	0.014	1	1	0.714	0.819	1.750	0.014	7.903	0.063	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.211	0.001	0.000	0.211	1	1	1.000	1.000	0.098	0.009	2.154	0.191	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.283	0.000	1	5	NaN	NaN	0.104	0.000	0.546	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.180	0.037	0.000	0.020	1	5	0.983	0.986	0.273	0.014	74.000	3.828	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	1	5	1.000	1.000	0.005	0.000	3.630	0.297	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.288	0.000	-1	5	NaN	NaN	0.102	0.000	0.543	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.290	0.000	0.000	0.032	-1	5	0.983	0.993	0.310	0.003	104.136	1.101	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.003	0.000	0.025	-1	5	1.000	1.000	0.005	0.000	4.720	0.698	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.282	0.000	-1	1	NaN	NaN	0.102	0.000	0.555	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.392	0.066	0.000	0.023	-1	1	0.974	0.989	0.262	0.002	89.330	0.775	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.002	0.000	0.020	-1	1	1.000	1.000	0.005	0.000	3.910	0.529	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.289	0.000	1	100	NaN	NaN	0.102	0.000	0.542	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.212	0.021	0.000	0.020	1	100	0.978	0.986	0.265	0.002	76.205	0.511	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.001	0.000	0.019	1	100	1.000	1.000	0.005	0.000	3.651	0.353	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.291	0.000	-1	100	NaN	NaN	0.102	0.000	0.537	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.236	0.000	0.000	0.032	-1	100	0.978	0.993	0.311	0.004	103.666	1.346	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.002	0.000	0.025	-1	100	1.000	1.000	0.005	0.000	4.592	0.476	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.290	0.000	1	1	NaN	NaN	0.102	0.000	0.538	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.992	0.028	0.000	0.011	1	1	0.974	0.989	0.262	0.002	41.943	0.273	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.000	2.837	0.282	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.970	0.000	0.027	0.000	-1	1	NaN	NaN	0.715	0.000	4.152	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.457	0.003	0.000	0.000	-1	1	0.974	0.974	0.195	0.002	2.347	0.031	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	0.000	0.000	0.000	6.946	3.205	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.986	0.000	0.027	0.000	-1	100	NaN	NaN	0.699	0.000	4.269	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.800	0.031	0.000	0.003	-1	100	0.976	0.972	0.579	0.010	4.834	0.099	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.009	0.001	0.000	0.009	-1	100	1.000	0.000	0.001	0.000	7.571	3.064	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.961	0.000	0.027	0.000	1	5	NaN	NaN	0.700	0.000	4.227	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.482	0.006	0.000	0.001	1	5	0.977	0.972	0.579	0.005	2.561	0.025	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	5	1.000	0.000	0.001	0.000	1.995	0.876	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.953	0.000	0.027	0.000	1	100	NaN	NaN	0.694	0.000	4.255	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.999	0.061	0.000	0.005	1	100	0.976	0.959	0.106	0.003	47.322	1.325	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.002	0.000	0.007	1	100	1.000	0.000	0.000	0.000	21.958	10.949	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.917	0.000	0.027	0.000	-1	5	NaN	NaN	0.692	0.000	4.218	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.854	0.007	0.000	0.001	-1	5	0.977	0.974	0.195	0.002	4.382	0.067	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	0.000	0.000	0.000	9.092	4.020	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.985	0.000	0.027	0.000	1	1	NaN	NaN	0.681	0.000	4.383	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.782	0.004	0.000	0.001	1	1	0.974	0.959	0.106	0.002	7.379	0.146	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	1	1.000	0.000	0.000	0.000	4.768	2.407	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.763	0.000	0.021	0.000	-1	1	NaN	NaN	0.439	0.000	1.735	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.002	0.001	0.000	-1	1	0.972	0.982	0.001	0.000	22.681	6.653	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.998	16.107	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.778	0.000	0.021	0.000	-1	100	NaN	NaN	0.445	0.000	1.748	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.001	0.000	0.000	-1	100	0.983	0.985	0.006	0.001	7.284	0.815	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	18.740	14.866	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.763	0.000	0.021	0.000	1	5	NaN	NaN	0.434	0.000	1.758	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.003	0.001	0.000	1	5	0.979	0.985	0.007	0.001	3.815	0.704	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.025	3.780	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.762	0.000	0.021	0.000	1	100	NaN	NaN	0.486	0.000	1.569	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.053	0.001	0.000	0.000	1	100	0.983	0.972	0.001	0.000	70.170	25.399	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.870	5.090	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.762	0.000	0.021	0.000	-1	5	NaN	NaN	0.454	0.000	1.678	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.000	0.001	0.000	-1	5	0.979	0.982	0.001	0.000	24.346	7.568	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	18.030	16.735	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.817	0.000	0.020	0.000	1	1	NaN	NaN	0.456	0.000	1.792	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	1	1	0.972	0.972	0.001	0.000	31.104	12.430	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.617	4.798	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.627	0.000	0.765	0.000	k-means++	NaN	30	NaN	0.400	0.0	1.569	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.381	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.577	5.591	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.631	8.029	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.494	0.000	0.971	0.000	random	NaN	30	NaN	0.391	0.0	1.265	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.379	0.000	random	0.001	30	0.001	0.000	0.0	8.214	4.908	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	9.932	7.218	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.683	0.000	3.591	0.000	k-means++	NaN	30	NaN	3.014	0.0	2.217	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	11.465	0.000	k-means++	0.002	30	0.002	0.000	0.0	7.328	5.073	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.019	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.626	6.220	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.170	0.000	3.890	0.000	random	NaN	30	NaN	3.350	0.0	1.842	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	15.291	0.000	random	0.002	30	0.002	0.000	0.0	5.313	3.063	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.772	7.205	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.229	0.0	0.014	0.000	k-means++	NaN	20	NaN	0.088	0.0	2.609	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.192	0.000	k-means++	-0.002	20	0.002	0.001	0.0	2.620	0.449	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.446	7.353	See	See
3	KMeans_short	fit	10000	10000	2	20	0.073	0.0	0.044	0.000	random	NaN	20	NaN	0.029	0.0	2.499	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.191	0.000	random	0.000	20	0.000	0.001	0.0	2.621	0.490	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.815	6.999	See	See
6	KMeans_short	fit	10000	10000	100	20	0.574	0.0	0.279	0.000	k-means++	NaN	20	NaN	0.327	0.0	1.756	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.697	0.000	k-means++	0.293	20	0.298	0.001	0.0	2.037	0.382	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.560	4.193	See	See
9	KMeans_short	fit	10000	10000	100	20	0.198	0.0	0.810	0.000	random	NaN	20	NaN	0.131	0.0	1.508	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.765	0.000	random	0.296	20	0.302	0.001	0.0	2.148	0.321	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.676	4.188	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.347	0.0	[-0.10398369]	0.000	NaN	NaN	NaN	NaN	NaN	2.085	0.0	5.442	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.5790345]	0.000	NaN	NaN	NaN	NaN	0.536	0.000	0.0	0.817	0.467	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.24242094]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.384	0.413	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.800	0.0	[2.59987395]	0.001	NaN	NaN	NaN	NaN	NaN	0.820	0.0	0.975	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[128.89072613]	0.000	NaN	NaN	NaN	NaN	0.300	0.003	0.0	0.529	0.110	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[23.44689673]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.122	0.089	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.189	0.000	0.424	0.0	NaN	NaN	NaN	0.190	0.000	0.996	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.599	0.0	NaN	NaN	0.138	0.020	0.001	0.601	0.032	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.212	0.0	NaN	NaN	NaN	0.000	0.000	0.655	0.716	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.439	0.000	0.556	0.0	NaN	NaN	NaN	0.252	0.000	5.706	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	4.651	0.0	NaN	NaN	1.000	0.000	0.000	0.732	0.544	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.013	0.0	NaN	NaN	NaN	0.000	0.000	0.674	0.804	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()

Scatter

Permutated curves

25th percentile

75th percentile